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AykeeSalazar
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vc-bantai-vit-withoutAMBI-adunest-v1

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update model card README.md

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@@ -15,11 +15,11 @@ model-index:
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  dataset:
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  name: imagefolder
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  type: imagefolder
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- args: Violation-Classification---Raw-9
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8039014373716632
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,8 +29,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7736
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- - Accuracy: 0.8039
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  ## Model description
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@@ -62,36 +62,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 0.4 | 100 | 1.2037 | 0.4081 |
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- | No log | 0.8 | 200 | 0.9935 | 0.4410 |
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- | No log | 1.2 | 300 | 0.6461 | 0.6915 |
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- | No log | 1.61 | 400 | 0.4938 | 0.7705 |
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- | 0.357 | 2.01 | 500 | 0.4602 | 0.7844 |
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- | 0.357 | 2.41 | 600 | 0.5220 | 0.7295 |
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- | 0.357 | 2.81 | 700 | 0.4665 | 0.7782 |
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- | 0.357 | 3.21 | 800 | 0.4440 | 0.8301 |
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- | 0.357 | 3.61 | 900 | 0.5122 | 0.7177 |
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- | 0.2437 | 4.02 | 1000 | 0.6155 | 0.7320 |
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- | 0.2437 | 4.42 | 1100 | 0.5802 | 0.7685 |
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- | 0.2437 | 4.82 | 1200 | 0.4709 | 0.8029 |
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- | 0.2437 | 5.22 | 1300 | 0.4694 | 0.8352 |
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- | 0.2437 | 5.62 | 1400 | 0.4652 | 0.8203 |
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- | 0.1841 | 6.02 | 1500 | 0.5424 | 0.7649 |
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- | 0.1841 | 6.43 | 1600 | 0.4616 | 0.8060 |
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- | 0.1841 | 6.83 | 1700 | 0.3569 | 0.8547 |
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- | 0.1841 | 7.23 | 1800 | 0.3652 | 0.8737 |
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- | 0.1841 | 7.63 | 1900 | 0.7778 | 0.7438 |
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- | 0.1328 | 8.03 | 2000 | 0.5460 | 0.8162 |
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- | 0.1328 | 8.43 | 2100 | 0.8070 | 0.7767 |
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- | 0.1328 | 8.84 | 2200 | 0.6873 | 0.7798 |
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- | 0.1328 | 9.24 | 2300 | 0.8943 | 0.7782 |
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- | 0.1328 | 9.64 | 2400 | 0.5378 | 0.8552 |
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- | 0.1059 | 10.04 | 2500 | 0.7081 | 0.8070 |
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- | 0.1059 | 10.44 | 2600 | 0.9941 | 0.7012 |
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- | 0.1059 | 10.84 | 2700 | 0.9152 | 0.7900 |
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- | 0.1059 | 11.24 | 2800 | 0.7494 | 0.7736 |
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- | 0.1059 | 11.65 | 2900 | 0.7681 | 0.7870 |
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- | 0.081 | 12.05 | 3000 | 0.7736 | 0.8039 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  dataset:
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  name: imagefolder
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  type: imagefolder
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+ args: Violation-Classification---Raw-6
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9181222707423581
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3318
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+ - Accuracy: 0.9181
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.23 | 100 | 0.3365 | 0.8581 |
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+ | No log | 0.45 | 200 | 0.3552 | 0.8472 |
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+ | No log | 0.68 | 300 | 0.3165 | 0.8581 |
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+ | No log | 0.91 | 400 | 0.2882 | 0.8690 |
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+ | 0.3813 | 1.13 | 500 | 0.2825 | 0.8745 |
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+ | 0.3813 | 1.36 | 600 | 0.2686 | 0.9007 |
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+ | 0.3813 | 1.59 | 700 | 0.2381 | 0.9017 |
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+ | 0.3813 | 1.81 | 800 | 0.3643 | 0.8734 |
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+ | 0.3813 | 2.04 | 900 | 0.2873 | 0.8930 |
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+ | 0.2736 | 2.27 | 1000 | 0.2236 | 0.9039 |
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+ | 0.2736 | 2.49 | 1100 | 0.2652 | 0.8723 |
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+ | 0.2736 | 2.72 | 1200 | 0.2793 | 0.8952 |
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+ | 0.2736 | 2.95 | 1300 | 0.2158 | 0.8974 |
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+ | 0.2736 | 3.17 | 1400 | 0.2410 | 0.8886 |
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+ | 0.2093 | 3.4 | 1500 | 0.2262 | 0.9017 |
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+ | 0.2093 | 3.63 | 1600 | 0.2110 | 0.9214 |
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+ | 0.2093 | 3.85 | 1700 | 0.2048 | 0.9138 |
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+ | 0.2093 | 4.08 | 1800 | 0.2044 | 0.9127 |
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+ | 0.2093 | 4.31 | 1900 | 0.2591 | 0.9007 |
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+ | 0.1764 | 4.54 | 2000 | 0.2466 | 0.8952 |
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+ | 0.1764 | 4.76 | 2100 | 0.2554 | 0.9017 |
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+ | 0.1764 | 4.99 | 2200 | 0.2145 | 0.9203 |
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+ | 0.1764 | 5.22 | 2300 | 0.3187 | 0.9039 |
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+ | 0.1764 | 5.44 | 2400 | 0.3336 | 0.9050 |
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+ | 0.1454 | 5.67 | 2500 | 0.2542 | 0.9127 |
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+ | 0.1454 | 5.9 | 2600 | 0.2796 | 0.8952 |
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+ | 0.1454 | 6.12 | 2700 | 0.2410 | 0.9181 |
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+ | 0.1454 | 6.35 | 2800 | 0.2503 | 0.9148 |
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+ | 0.1454 | 6.58 | 2900 | 0.2966 | 0.8996 |
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+ | 0.1216 | 6.8 | 3000 | 0.1978 | 0.9312 |
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+ | 0.1216 | 7.03 | 3100 | 0.2297 | 0.9214 |
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+ | 0.1216 | 7.26 | 3200 | 0.2768 | 0.9203 |
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+ | 0.1216 | 7.48 | 3300 | 0.3356 | 0.9083 |
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+ | 0.1216 | 7.71 | 3400 | 0.3415 | 0.9138 |
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+ | 0.1038 | 7.94 | 3500 | 0.2398 | 0.9061 |
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+ | 0.1038 | 8.16 | 3600 | 0.3347 | 0.8963 |
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+ | 0.1038 | 8.39 | 3700 | 0.2199 | 0.9203 |
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+ | 0.1038 | 8.62 | 3800 | 0.2943 | 0.9061 |
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+ | 0.1038 | 8.84 | 3900 | 0.2561 | 0.9181 |
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+ | 0.0925 | 9.07 | 4000 | 0.4170 | 0.8777 |
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+ | 0.0925 | 9.3 | 4100 | 0.3638 | 0.8974 |
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+ | 0.0925 | 9.52 | 4200 | 0.3233 | 0.9094 |
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+ | 0.0925 | 9.75 | 4300 | 0.3496 | 0.9203 |
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+ | 0.0925 | 9.98 | 4400 | 0.3621 | 0.8996 |
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+ | 0.0788 | 10.2 | 4500 | 0.3260 | 0.9116 |
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+ | 0.0788 | 10.43 | 4600 | 0.3979 | 0.9061 |
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+ | 0.0788 | 10.66 | 4700 | 0.3301 | 0.8974 |
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+ | 0.0788 | 10.88 | 4800 | 0.2197 | 0.9105 |
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+ | 0.0788 | 11.11 | 4900 | 0.3306 | 0.9148 |
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+ | 0.0708 | 11.34 | 5000 | 0.3318 | 0.9181 |
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  ### Framework versions